Method Research On Multi-level Semantic Analysis Of Surveillance Video

With the rapid development of computer vision technology,the data of video is growing with an amazing speed.At present,it has become more and more common for the slogan that "smart city and intelligent site" is called in the construction projects.The research of video has become an important part of monitoring security and intelligent.Therefore,it becomes an urgent problem to solve the problem of accurate semantic analysis and getting accurate event information of the surveillance video.Video semantic analysis and prediction is the engine and core element of the intelligent city.The related technology to deal with the perception of multi-dimensional data structure is an inevitable choice.In this thesis,some problems are studied such as extracting the moving objects from the complex scene,exact recognition and retrieval of the video objects,semantic analysis of the specific events,related semantic analysis of the video objects,and so on.In combination with these aspects,this thesis presents a multi-level monitoring video semantic analysis method that includes scenes,objects and events.In order to analyze the semantic information of the video and use it for video retrieval in a better way,a multi-level video semantic analysis model is constructed.Firstly,the low-level features are extracted and analyzed,including texture features,color features,shape features and motion characteristics.To match the detection,the subsequent KNN is facilitated to extract the characteristics and quantify the analysis.Then,the relevant research is carried out on the scene,object and event.And the scene is analyzed by an improved Vibe algorithm.The ontology concept and the attribute analysis are added to analyze the object.In order to facilitate the match of subsequent,concept of object in the video frame is vectorized.The correctness and validity of the method about events analysis can be verified by the means of rule definition and matching experiments.The multi-level video semantic model has not only the semantics extracted from different levels,but also the semantic relations between hierarchical semantics.To make full use of the attribute between the object and level association,the semantics of the surveillance video and labelling is extracted,and the formally describe work by the MVSS(Multilayer Video Semantic Symbolic representation)method is done.Finally the multi-level semantic analysis and annotation method is adopted to extract video semantic from different levels,and annotate and retrieve the information.In order to analyze the sequence of the video frame,the scene and target object are identified and analyzed,and the event occurring in the video frame is detected.What's more,an effective hierarchical correlation analysis method of video semantic features,semantic annotation and retrieval methods which are suitable for surveillance video data are proposed in order to reduce the semantic gap between low-level and high-level semantic mapping.The work has been done in thesis is to give a way of thinking and direction in providing a certain method for the video data analysis,the construction work and daily video information analysis.Finally,a multi-level semantic analysis method is proposed to design a retrieval system to show the experimental results of this thesis.Multi-level video semantics enriches the content of video semantic annotation,which provides a good basis for retrieving video.The final matching experimental results show that the proposed method can do a better job in detecting the target object and event,and getting the true information.